7 citations · 14 across the 7 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
Alexander Stollenwerk, Laurent Jacques
We propose a novel algorithm for distributed stochastic gradient descent (SGD) with compressed gradient communication in the parameter-server framework. Our gradient compression te…
cs.LG2021
The Separation Capacity of Random Neural Networks
Sjoerd Dirksen, Martin Genzel, Laurent Jacques +1
Neural networks with random weights appear in a variety of machine learning applications, most prominently as the initialization of many deep learning algorithms and as a computati…